• Title/Summary/Keyword: 3D face generation

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A Study on Production of Low Storage Capacity of Character Animation for 3D Mobile Games (3D 모바일 게임용 저용량 3D캐릭터 애니메이션 제작에 관한 연구)

  • Lee Ji-Won;Kim Tae-Yul;Kyung Byung-Pyo
    • The Journal of the Korea Contents Association
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    • v.5 no.5
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    • pp.107-114
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    • 2005
  • The next generation of 3D mobile games market is becoming increasingly active, more as a result of improvement in the CPU speed of hardware phone, embarkation of 3D engines and high memory capacity, In response to this trend, popular 3D games of PS2 (PlayStation2) and popular online games are being launched as mobile games. However, mobile units have different hardware characteristics compared to those of other platforms such as the PC or the game console. Therefore, mobile game versions of the popular PC games face many limitations in many aspects such as in battery capacity, size of display, capacity of the game, and other user interface issues. Among these many limitations, study for allowing low capacity storage of the game is becoming important. In addition, realistic animation of the 3D character on the small screen of the mobile unit is more important than any other matter. The purpose of this study is to find a solution to providing realistic 3D character animation, and for decreasing the storage capacity of character animation for application in 3D mobile games.

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A Study on the implementation of the drape generation model using textile drape image (섬유 드레이프 이미지를 활용한 드레이프 생성 모델 구현에 관한 연구)

  • Son, Jae Ik;Kim, Dong Hyun;Choi, Yun Sung
    • Smart Media Journal
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    • v.10 no.4
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    • pp.28-34
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    • 2021
  • Drape is one of the factors that determine the shape of clothes and is one of the very important factors in the textile and fashion industry. At a time when non-face-to-face transactions are being activated due to the impact of the coronavirus, more and more companies are asking for drape value. However, in the case of small and medium-sized enterprises (SMEs), it is difficult to measure the drape, because they feel the burden of time and money for measuring the drape. Therefore, this study aimed to generate a drape image for the material property value input using a conditional adversarial neural network through 3D simulation images generated by measuring digital properties. A drape image was created through the existing 736 digital property values, and this was used for model training. Then, the drape value was calculated for the image samples obtained through the generative model. As a result of comparing the actual drape experimental value and the generated drape value, it was confirmed that the error of the peak number was 0.75, and the average error of the drape value was 7.875

Zigzag Tool-Path Linking Algorithm for Shaping Process Using Heat Source (열원을 이용한 공정에서 지그재그 공구 경로 연결 알고리즘)

  • Kim H. C.;Lee S. H.;Yang D. Y.
    • Korean Journal of Computational Design and Engineering
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    • v.9 no.4
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    • pp.286-293
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    • 2004
  • Recently, hot processing using the heat source like laser machining and RFS was developed and spreaded gradually. In order to generate tool-path for the proper hot tool, a new tool-path linking algorithm is needed because tool-path linking algorithm for machining can't be applied. In this paper, zigzag tool-path liking algorithm was proposed to generate tool-path automatically for RFS. The algorithm is composed of three steps: 1) Generating valid tool-path element, 2) Storing tool-path elements and creating sub-groups, 3) linking sub-groups. Using the proposed algorithm, CAD/CAM software for the tool-path generation of hot tool was developed. The proposed algorithm was applied and verified for Venus's face and die of cellular phone case.

Realistic individual 3D face modeling (사실적인 3D 얼굴 모델링 시스템)

  • Kim, Sang-Hoon
    • The Journal of the Korea institute of electronic communication sciences
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    • v.8 no.8
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    • pp.1187-1193
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    • 2013
  • In this paper, we present realistic 3D head modeling and facial expression systems. For 3D head modeling, we perform generic model fitting to make individual head shape and texture mapping. To calculate the deformation function in the generic model fitting, we determine correspondence between individual heads and the generic model. Then, we reconstruct the feature points to 3D with simultaneously captured images from calibrated stereo camera. For texture mapping, we project the fitted generic model to image and map the texture in the predefined triangle mesh to generic model. To prevent extracting the wrong texture, we propose a simple method using a modified interpolation function. For generating 3D facial expression, we use the vector muscle based algorithm. For more realistic facial expression, we add the deformation of the skin according to the jaw rotation to basic vector muscle model and apply mass spring model. Finally, several 3D facial expression results are shown at the end of the paper.

A 3D Game Character Design Using MAYA (MAYA를 이용한 3D게임 캐릭터 디자인)

  • Ryu, Chang-Su;Hur, Chang-Wu
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.15 no.6
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    • pp.1333-1337
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    • 2011
  • 3D engines loading, and expansion of the usable capacity, next-generation smartphone game markets are rising briskly by the improvement in CPU processing speed of Phones (hardware of smartphone). Therefore, in creating 3D game characters, realistic and free-form animations in a small screen of a smartphone are becoming important. Through this paper, as a method of creating characters and operating for game characters to cause user's feeling, with NURBS data of MAYA, We completed a face in turns of eyes, a nose, and a mouth, and with Polygon Cube tool, modeled hands and feet. After dividing a cube into half and modeling it, through mirror copying We completed the whole body and modeled the low-polygon. Then to model realistic and free-form characters, We completed each detail with ZBrush and applied Divide level up to 4. Though they might look rough and exaggerated, We tried to express stuck-out parts and fallen-in parts effectively and smoothly with Smooth brush effect, map and design the low-polygon 3D characters.

A 3D Game Character Design Using MAYA (MAYA를 이용한 3D게임 캐릭터 디자인)

  • Ryu, Chang-Su;Hur, Chang-Wu
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2011.05a
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    • pp.300-303
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    • 2011
  • Owing to the improvement in CPU processing speed of Phones (hardware of smartphone), 3D engines loading, and expansion of the usable capacity, next-generation smartphone game markets are rising briskly. Therefore, in creating 3D game characters, realistic and free-form animations in a small screen of a smartphone are becoming important. Through this paper, as a method of creating characters and operating for game characters to cause user's feeling, with NURBS data of MAYA, We completed a face in turns of eyes, a nose, and a mouth, and with Polygon Cube tool, modeled hands and feet. After dividing a cube into half and modeling it, through mirror copying We completed the whole body and modeled the low-polygon. Then to model realistic and free-form characters, We completed each detail with ZBrush and applied Divide level up to 4. Though they might look rough and exaggerated, We tried to express stuck-out parts and fallen-in parts effectively and smoothly with Smooth brush effect, map and design the low-polygon 3D characters.

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Robust 3D Facial Landmark Detection Using Angular Partitioned Spin Images (각 분할 스핀 영상을 사용한 3차원 얼굴 특징점 검출 방법)

  • Kim, Dong-Hyun;Choi, Kang-Sun
    • Journal of the Institute of Electronics and Information Engineers
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    • v.50 no.5
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    • pp.199-207
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    • 2013
  • Spin images representing efficiently surface features of 3D mesh models have been used to detect facial landmark points. However, at a certain point, different normal direction can lead to quite different spin images. Moreover, since 3D points are projected to the 2D (${\alpha}-{\beta}$) space during spin image generation, surface features cannot be described clearly. In this paper, we present a method to detect 3D facial landmark using improved spin images by partitioning the search area with respect to angle. By generating sub-spin images for angular partitioned 3D spaces, more unique features describing corresponding surfaces can be obtained, and improve the performance of landmark detection. In order to generate spin images robust to inaccurate surface normal direction, we utilize on averaging surface normal with its neighboring normal vectors. The experimental results show that the proposed method increases the accuracy in landmark detection by about 34% over a conventional method.

Visualization of Tunneling Using a BIM-based 3D Tunnel Model (BIM 기반 3D 터널 모델 가시화에 관한 연구)

  • Yoo, Wan-Kyu;Kim, Jinhwan;Zheng, Xiumei;Kim, Jeong-Heum;Gi, Sang-bok;Kim, Chang-Yong
    • The Journal of Engineering Geology
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    • v.25 no.3
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    • pp.395-401
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    • 2015
  • An investigation of the tunnel face, as well as related measurement data collected during tunneling, is necessary for rock classification and to determine tunnel stability and the cost efficiency of tunneling. However, systematic management and efficient use of such data have yet to be successfully implemented domestically, and the number of experts in this field in Korea is limited. Thus, measures to develop and implement systematic management and effective use of data and expertise are urgently needed. This study aimed to develop measures to efficiently provide online tunnel design and construction data using a building information model (BIM)-based data visualization approach, based on an integrated 3D tunnel model generation module and a web viewer module. The development technology was verified through ○○ tunnel design and construction. Directions for future study and system improvement are proposed.

Automatic Anticipation Generation for 3D Facial Animation (3차원 얼굴 표정 애니메이션을 위한 기대효과의 자동 생성)

  • Choi Jung-Ju;Kim Dong-Sun;Lee In-Kwon
    • Journal of KIISE:Computer Systems and Theory
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    • v.32 no.1
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    • pp.39-48
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    • 2005
  • According to traditional 2D animation techniques, anticipation makes an animation much convincing and expressive. We present an automatic method for inserting anticipation effects to an existing facial animation. Our approach assumes that an anticipatory facial expression can be found within an existing facial animation if it is long enough. Vertices of the face model are classified into a set of components using principal components analysis directly from a given hey-framed and/or motion -captured facial animation data. The vortices in a single component will have similar directions of motion in the animation. For each component, the animation is examined to find an anticipation effect for the given facial expression. One of those anticipation effects is selected as the best anticipation effect, which preserves the topology of the face model. The best anticipation effect is automatically blended with the original facial animation while preserving the continuity and the entire duration of the animation. We show experimental results for given motion-captured and key-framed facial animations. This paper deals with a part of broad subject an application of the principles of traditional 2D animation techniques to 3D animation. We show how to incorporate anticipation into 3D facial animation. Animators can produce 3D facial animation with anticipation simply by selecting the facial expression in the animation.

Face Morphing Using Generative Adversarial Networks (Generative Adversarial Networks를 이용한 Face Morphing 기법 연구)

  • Han, Yoon;Kim, Hyoung Joong
    • Journal of Digital Contents Society
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    • v.19 no.3
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    • pp.435-443
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    • 2018
  • Recently, with the explosive development of computing power, various methods such as RNN and CNN have been proposed under the name of Deep Learning, which solve many problems of Computer Vision have. The Generative Adversarial Network, released in 2014, showed that the problem of computer vision can be sufficiently solved in unsupervised learning, and the generation domain can also be studied using learned generators. GAN is being developed in various forms in combination with various models. Machine learning has difficulty in collecting data. If it is too large, it is difficult to refine the effective data set by removing the noise. If it is too small, the small difference becomes too big noise, and learning is not easy. In this paper, we apply a deep CNN model for extracting facial region in image frame to GAN model as a preprocessing filter, and propose a method to produce composite images of various facial expressions by stably learning with limited collection data of two persons.